Classifying subgroups of patients with symptoms of acute coronary syndromes: A cluster analysis

Symptom clusters and unplanned hospital readmission in Chinese patients with acute myocardial infarction on admission

Backgroud

Acute myocardial infarction (AMI) has a high morbidity rate, high mortality rate, high readmission rate, high health care costs, and a high symptomatic, psychological, and economic burden on patients. Patients with AMI usually present with multiple symptoms simultaneously, which are manifested as symptom clusters. Symptom clusters have a profound impact on the quality of survival and clinical outcomes of AMI patients.

Objective

The purpose of this study was to analyze unplanned hospital readmissions among cluster groups within a 1-year follow-up period, as well as to identify clusters of acute symptoms and the characteristics associated with them that appeared in patients with AMI.

Methods

Between October 2021 and October 2022, 261 AMI patients in China were individually questioned for symptoms using a structured questionnaire. Mplus 8.3 software was used to conduct latent class analysis in order to find symptom clusters. Univariate analysis is used to examine characteristics associated with each cluster, and multinomial logistic regression is used to analyze a cluster membership as an independent predictor of hospital readmission after 1-year.

Results

Three unique clusters were found among the 11 acute symptoms: the typical chest symptom cluster (64.4%), the multiple symptom cluster (29.5%), and the atypical symptom cluster (6.1%). The cluster of atypical symptoms was more likely to have anemia and the worse values of Killip class compared with other clusters. The results of multiple logistic regression indicated that, in comparison to the typical chest cluster, the atypical symptom cluster substantially predicted a greater probability of 1-year hospital readmission (odd ratio 8.303, 95% confidence interval 2.550-27.031, P < 0.001).

Conclusion

Out of the 11 acute symptoms, we have found three clusters: the typical chest symptom, multiple symptom, and atypical symptom clusters. Compared to patients in the other two clusters, those in the atypical symptom cluster-which included anemia and a large percentage of Killip class patients-had worse clinical indicators at hospital readmission during the duration of the 1-year follow-up. Both anemia and high Killip classification suggest that the patient's clinical presentation is poor and therefore the prognosis is worse. Intensive treatment should be considered for anemia and high level of Killip class patients with atypical presentation. Clinicians should focus on patients with atypical symptom clusters, enhance early recognition of symptoms, and develop targeted symptom management strategies to alleviate their discomfort in order to improve symptomatic outcomes.

Symptoms of Acute Myocardial Infarction as Described in Calls to Tele-Nurses and in Questionnaires: A Mixed-Methods Study

BACKGROUND

Patient-reported symptoms of acute myocardial infarction (MI) may be affected by recall bias depending on when and where symptoms are assessed.

AIM

The aim of this study was to gain an understanding of patients' symptom description in more detail before and within 24 hours after a confirmed MI diagnosis.

METHODS

A convergent parallel mixed-methods design was used to examine symptoms described in calls between the tele-nurse and the patient compared with symptoms selected by the patient from a questionnaire less than 24 hours after hospital admission. Quantitative and qualitative data were analyzed separately and then merged into a final interpretation.

RESULTS

Thirty patients (median age, 67.5 years; 20 men) were included. Chest pain was the most commonly reported symptom in questionnaires (24/30). Likewise, in 19 of 30 calls, chest pain was the first complaint mentioned, usually described together with the symptom onset. Expressions used to describe symptom quality were pain, pressure, discomfort, ache, cramp, tension, and soreness. Associated symptoms commonly described were pain or numbness in the arms, cold sweat, dyspnea, weakness, and nausea. Bodily sensations, such as feeling unwell or weak, were also described. Fear and tiredness were described in calls significantly less often than reported in questionnaires ( P = .01 and P = .02), whereas "other" symptoms were more often mentioned in calls compared with answers given in the questionnaire ( P = .02). Some symptoms expressed in the calls were not listed in the questionnaire, which expands the understanding of acute MI symptoms. The results showed no major inconsistencies between datasets.

CONCLUSION

Patients' MI symptom descriptions in tele-calls and those reported in questionnaires after diagnosis are comparable and convergent.

Characteristic differences of chest pain in male and female patients with acute coronary syndrome: A pilot study

Background: The typical sign or main symptom in acute coronary syndrome (ACS) patients is chest pain, which is an initial benchmark or early sign for diagnosis. Certain factors, such as gender differences, the presence of diabetes mellitus or other clinical conditions, may make the patient not realize they have ACS. Therefore, this study aims to identify the characteristics of chest pain symptoms in male and female patients with ACS.

Design and Methods: This is a non-experimental quantitative study, namely analytical observation using a cross-sectional approach within 4 months (January-April 2019). Furthermore, the samples were 53 ACS patients (28 male and 25 female).

Results: The chest pain characteristics that have a significant relationship with gender differences in ACS patients are shown based on the aspects of location, pain duration and quality. Male patients are more likely to feel pain at the left or middle chest, the duration is between <20 to >20 min with moderate pain quality, which tends to become severe, while females are more likely to feel pain at the chest which radiates to the neck and chin, the duration is usually >20 min, with mild to moderate pain quality.

Conclusions: The result showed a significant difference in chest pain characteristics in male and female patients with ACS. Regarding location, duration and quality of chest pain, male ACS patients mostly have more typical symptoms, while females’ symptoms are atypical.

Chest pain symptoms differences between diabetes mellitus and non-diabetes mellitus patients with acute coronary syndrome: A pilot study

BACKGROUND

Chest pain is considered one of the crucial indicators in detecting acute coronary syndrome (ACS), and one of the most common complaints frequently found in hospitals. Atypical characteristics of chest pain have prevented patients from being aware of ACS. Chest pain symptoms have become ambiguous, particularly for specific parameters, such as gender, diabetes mellitus (DM), or other clinical conditions. Therefore, it is critical for high-risk patients to have adequate knowledge of specific symptoms of ACS, which is frequently associated with late treatment or prehospital delay. Therefore, this study aims to identify the particular characteristics of chest pain symptoms in DM and non-DM patients with ACS.

DESIGN AND METHODS

This is a quantitative and non-experimental research, with the cross-sectional approach used to carry out the analytical observation at a general hospital from January-April 2019. Data were obtained from a total sample of 61 patients, comprising 33 ACS with DM and 28 ACS non-DM patients.

RESULTS

The result showed that the characteristic of patients with chest pain symptoms has a significant relation to DM and ACS. Therefore, non-DM patients with ACS are more likely to feel chest pain at moderate to a severe level, while ACS-DM patients are more likely to have low to moderate chest pain levels.

CONCLUSION

The significant differences in the characteristics of chest pain in DM and non-DM patients suffering from acute coronary syndrome are the points of location of chest pain radiating to the neck and quality of pain.

Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility

BACKGROUND

Machine learning (ML) is increasingly used in research for subtype definition and risk prediction, particularly in cardiovascular diseases. No existing ML models are routinely used for cardiovascular disease management, and their phase of clinical utility is unknown, partly due to a lack of clear criteria. We evaluated ML for subtype definition and risk prediction in heart failure (HF), acute coronary syndromes (ACS) and atrial fibrillation (AF).

METHODS

For ML studies of subtype definition and risk prediction, we conducted a systematic review in HF, ACS and AF, using PubMed, MEDLINE and Web of Science from January 2000 until December 2019. By adapting published criteria for diagnostic and prognostic studies, we developed a seven-domain, ML-specific checklist.

RESULTS

Of 5918 studies identified, 97 were included. Across studies for subtype definition (n = 40) and risk prediction (n = 57), there was variation in data source, population size (median 606 and median 6769), clinical setting (outpatient, inpatient, different departments), number of covariates (median 19 and median 48) and ML methods. All studies were single disease, most were North American (n = 61/97) and only 14 studies combined definition and risk prediction. Subtype definition and risk prediction studies respectively had limitations in development (e.g. 15.0% and 78.9% of studies related to patient benefit; 15.0% and 15.8% had low patient selection bias), validation (12.5% and 5.3% externally validated) and impact (32.5% and 91.2% improved outcome prediction; no effectiveness or cost-effectiveness evaluations).

CONCLUSIONS

Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.

Symptom Profiles of Latina Breast Cancer Survivors: A Latent Class Analysis

BACKGROUND

Symptom research among Latinas with breast cancer is limited-especially as it relates to multiple co-occurring symptoms.

OBJECTIVE

The aim of the study was to identify subgroups (latent classes) of Latinas who have distinct symptom profiles while receiving radiation, chemotherapy, and/or hormonal therapy for breast cancer.

METHODS

This secondary analysis included intake data from three randomized trials of supportive care psychosocial interventions for Latinas treated for breast cancer (n = 290). Prevalence of 12 symptoms-measured using the General Symptom Distress Scale-was entered into the latent class analysis to identify classes of women with different symptom profiles.

RESULTS

Most of the participants had Stage II or III disease, and 81% reported receiving chemotherapy. On average, women reported 4.2 (standard deviation [SD] = 3) symptoms with an overall symptom distress score of 6.4 (SD = 2.5) on a 1-10 scale, with 10 being most distressing. Latent class analysis resulted in three classes that were labeled based on symptoms with the highest prevalence. Class 1 (n = 192) was "Disrupted Sleep and Tired," Class 2 (n = 74) was "Tired," and Class 3 (n = 24) was "Pain, Disrupted Sleep, and Tired." Depression, anxiety, and difficulty concentrating had moderate prevalence in each of the three classes.

DISCUSSION

Beyond the core six symptoms (depression, anxiety, fatigue, pain, disrupted sleep, difficulty concentration), the classes differed in the prevalence of other burdensome symptoms (e.g., nausea, vomiting, constipation), which provide implications for treatment. Thus, it is important to assess for the full range of symptoms so that supportive care interventions can be tailored for the distinct symptom profiles of Latinas with breast cancer.

Prevalence and Predictors of Delay in Seeking Emergency Care in Patients Who Call 9-1-1 for Chest Pain

BACKGROUND

Delay in seeking medical treatment for suspected acute coronary syndrome can lead to negative patient outcomes.

OBJECTIVE

Our aim was to evaluate the prevalence and predictors of delay in seeking care in high-risk chest pain patients with or without acute coronary syndrome (ACS).

METHODS

This was a secondary analysis of an observational cohort study of patients transported by Emergency Medical Services for a chief complaint of chest pain. Important demographic and clinical characteristics were extracted from electronic health records. Two independent reviewers adjudicated the presence of ACS. Logistic regression was used to model the predictors of delay in seeking care.

RESULTS

The final sample included 743 patients (99% non-Hispanic). Overall, 24% presented > 12 h from onset of symptoms. Among those with ACS (n = 115), 14% presented > 12 h after onset of symptoms. Race, smoking, diabetes, and related symptoms were associated with delayed seeking behavior. In multivariate analysis, non-Caucasian race (black or others) was the only independent predictor of > 12 h delay in seeking care (odds ratio 1.4; 95% confidence interval 1.0-1.9).

CONCLUSIONS

One in four patients with chest pain, including 14% of those with ACS, wait more than 12 h before seeking care. Compared to non-blacks, black patients are 40% more likely to delay seeking care > 12 h.

Protein Cytokines, Cytokine Gene Polymorphisms, and Potential Acute Coronary Syndrome Symptoms

The purpose of this study was to determine whether relationships exist among protein cytokines, cytokine gene polymorphisms, and symptoms of potential acute coronary syndrome (ACS). Participants included 438 patients presenting to the emergency department (ED) whose symptoms triggered a cardiac evaluation (206 ruled in and 232 ruled out for ACS). Presence or absence of 13 symptoms was recorded upon arrival. Levels of tumor necrosis factor α (TNF-α), interleukin (IL)-6, and IL-18 were measured for all patients. A pilot analysis of 85 patients (ACS = 49; non-ACS = 36) genotyped eight single-nucleotide polymorphisms (SNPs; four TNF and four IL6 SNPs). Logistic regression models were tested to determine whether cytokines or SNPs predicted symptoms. Increased levels of TNF-α and IL-6 were associated with a decreased likelihood of chest discomfort for all patients. Increased levels of IL-6 were associated with a lower likelihood of chest discomfort and chest pressure for ACS patients, and an increased likelihood of shoulder and upper back pain for non-ACS patients. Elevated IL-18 was associated with an increased likelihood of sweating in patients with ACS. Of the four TNF SNPs, three were associated with shortness of breath, lightheadedness, unusual fatigue, and arm pain. In all, protein cytokines and TNF polymorphisms were associated with 11 of 13 symptoms assessed. Future studies are needed to determine the predictive ability of cytokines and related SNPs for a diagnosis of ACS or to determine whether biomarkers can identify patients with specific symptom clusters.

The Quality of Symptoms in Women and Men Presenting to the Emergency Department With Suspected Acute Coronary Syndrome

INTRODUCTION

More than 5.5 million patients present to emergency departments in the United States annually for potential acute coronary syndrome (ACS); however, diagnosing ACS remains a challenge in emergency departments. Our aim was to describe the quality of symptoms (chest discomfort/description of pain, location/radiation, and overall symptom distress) reported by women and men ruled-in and ruled-out for ACS in emergency departments.

METHODS

The sample consisted of 1,064 patients presenting to emergency departments with symptoms that triggered cardiac workups. Trained research staff obtained data using the ACS Patient Information Questionnaire upon patient presentation to emergency departments.

RESULTS

The sample (n = 1,064) included 474 (44.55%) patients ruled-in and 590 (55.45%) patients ruled-out for ACS. Symptom distress was significantly higher in patients ruled-in versus ruled-out for ACS (7.3 ± 2.6 vs. 6.8 ± 2.5; P = 0.002) and was a significant predictor for an ACS diagnosis in men (odds ratio [OR], 1.10; confidence interval [CI], 1.03-1.17; P = 0.003). Women also reported more chest pressure (51.75% vs. 44.65; P = 0.02) compared with men, and chest pressure was a significant predictor for a diagnosis of ACS (OR, 1.61; CI, 1.03-2.53; P = 0.02).

DISCUSSION

Higher levels of symptom distress may help ED personnel in making a decision to evaluate a patient for ACS, and the presence of chest pressure may aid in making a differential diagnosis of ACS.

Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods

Researchers have employed various methods to identify symptom clusters in cardiovascular conditions, without identifying rationale. Here, we test clustering techniques and outcomes using a data set from patients with acute coronary syndrome. A total of 474 patients who presented to emergency departments in five United States regions were enrolled. Symptoms were assessed within 15 min of presentation using the validated 13-item ACS Symptom Checklist. Three variable-centered approaches resulted in four-factor solutions. Two of three person-centered approaches resulted in three-cluster solutions. K-means cluster analysis revealed a six-cluster solution but was reduced to three clusters following cluster plot analysis. The number of symptoms and patient characteristics varied within clusters. Based on our findings, we recommend using (a) a variable-centered approach if the research is exploratory, (b) a confirmatory factor analysis if there is a hypothesis about symptom clusters, and (c) a person-centered approach if the aim is to cluster symptoms by individual groups.

Elaboration and validation of an ICNP® terminology subset for patients with acute myocardial infarction

ABSTRACT Objective: To elaborate a terminological subset for the International Classification for Nursing Practice (ICNP®) for patients with acute myocardial infarction using the Activities of Living Model. Method: A methodological study which followed the guidelines of the International Nursing Council and was based on theoretical framework of the Activities of Living Model for its elaboration. Content validation was performed by 22 nursing specialists. Results: Twenty-two (22) diagnoses and 22 nursing outcomes were elaborated. Of these, 17 nursing diagnosis statements and 17 nursing outcome statements presented Content Validity Index (CVI) ≥ 0.80. Of the 113 elaborated nursing interventions, 42 reached a CVI ≥ 0.80, and 51 interventions made up the terminological subset after the expert suggestions. Conclusion: The ICNP® was suitable for use with the Activities of Living Model, having compatible terms with those used in clinical nursing practice, and valid for construction of the terminological subset for patients with acute myocardial infarction and most likely to facilitate clinical nursing judgment.

Symptom Cluster Science in Chronic Kidney Disease: A Literature Review

The purpose of this review was to synthesize evidence on symptom clusters in patients with chronic kidney disease (CKD). The quality of studies was evaluated using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Twelve articles met inclusion criteria. Patients had CKD ranging from Stages 2 through 5. Most studies determined clusters using variable-centered approaches based on symptoms; however, one used a person-centered approach based on demographic and clinical characteristics. The number of clusters identified ranged from two to five. Several clusters were prominent across studies including symptom dimensions of fatigue/energy/sleep, neuromuscular/pain, gastrointestinal, skin, and uremia; however, individual symptoms assigned to clusters varied widely. Several clusters correlated with patient outcomes, including health-related quality of life and mortality. Identifying symptom clusters in CKD is a nascent field, and more research is needed on symptom measures and statistical methods for clustering. The clinical implications of symptom clusters remain unclear.

Patient-reported symptoms improve prediction of acute coronary syndrome in the emergency department

Early diagnosis is critical in the management of patients with acute coronary syndrome (ACS), particularly ST-elevation myocardial infarction (STEMI), because effective therapies are time-dependent. Aims of this secondary analysis were to determine: (i) the prognostic value of symptoms for an ACS diagnosis in conjunction with electrocardiographic (ECG) and troponin results; and (ii) if any of 13 symptoms were associated with prehospital delay in those presenting to the emergency department (ED) with potential ACS. Patients receiving a cardiac evaluation in the ED were eligible for the study. Thirteen patient-reported symptoms were assessed in triage. Prehospital delay time was calculated as the time from symptom onset until registration in the ED. A total of 1,064 patients were enrolled in five EDs. The sample was 62% male, 70% white, and had a mean age of 60.2 years. Of 474 participants diagnosed with ACS, 118 (25%) had STEMI; 251 (53%) had non-ST elevation myocardial infarction (NSTEMI); and 105 (22%) had unstable angina. Sweating (OR = 1.42 CI [1.01, 2.00]) and shoulder pain (OR = 1.64 CI [1.13, 2.38]) added to the predictive value of an ACS diagnosis when combined with ECG and troponin results. Shortness of breath (OR = 0.71 CI [0.50, 1.00]) and unusual fatigue (OR = 0.60 CI [0.42, 0.84]) were predictive of a non-ACS diagnosis. Sweating predicted shorter prehospital delay (HR = 1.35, CI [1.10, 1.67]); shortness of breath (HR = 0.73 CI [0.60, 0.89]) and unusual fatigue (HR = 0.72, CI [0.57, 0.90]) were associated with longer prehospital delay. Patient-reported symptoms are significantly associated with ACS diagnoses and prehospital delay. Sweating and shoulder pain combined with ECG signs of ischemia may improve the timely detection of ACS in the ED.

The Effect of Media Campaigns, Patient Characteristics, and Presenting Symptoms on Prehospital Delay in Myocardial Infarction Patients: A Prospective Cohort Study

BACKGROUND

Delays in reperfusion therapy for myocardial infarction (MI) are associated with increased mortality and morbidity, and most of this delay is due to delay in patients initiating contact with emergency services. This study assesses the impact of the Australian National Heart Foundation media campaign and identifies patient characteristics and presenting symptoms that may contribute to delay.

METHODS

This prospective cohort study identified patients with a diagnosis of MI admitted to a single tertiary metropolitan hospital in Perth, Western Australia from July 2013 to January 2014. Patients were interviewed and responses were categorised to determine their reasons for delaying treatment and the impact of mass media campaigns. Delay times were analysed using multivariable linear regression models for the Whole Cohort (all patients admitted to the tertiary hospital, including patients from rural and peripheral hospitals) and the Direct Admission Cohort (patients admitted directly to the tertiary hospital).

RESULTS

Of 376 patients, 255 patients provided consent, and symptom onset-time was available for 175 patients. While almost two thirds of the cohort was aware of media campaigns, awareness was not associated with decreased prehospital delay. Median delay was 3.9hours for the Whole Cohort and 3.5hours for the Direct Admission Cohort. Delay was associated with being widowed, symptom onset on a weekday compared with weekend, past medical history of MI and coronary artery bypass graft, private compared with ambulance transport to hospital, and lack of symptoms of sweating and weakness. In addition, for the Direct Admission Cohort, age and income were also associated with delay.

CONCLUSIONS

This study did not find an association between awareness of media campaigns and delay. This study identified important characteristics and presenting symptoms that are associated with delay, and possibly relevant to future media campaigns.

Symptom clusters and treatment time delay in Korean patients with ST-elevation myocardial infarction on admission

Most patients with acute myocardial infarction (AMI) experience more than one symptom at onset. Although symptoms are an important early indicator, patients and physicians may have difficulty interpreting symptoms and detecting AMI at an early stage. This study aimed to identify symptom clusters among Korean patients with ST-elevation myocardial infarction (STEMI), to examine the relationship between symptom clusters and patient-related variables, and to investigate the influence of symptom clusters on treatment time delay (decision time [DT], onset-to-balloon time [OTB]). This was a prospective multicenter study with a descriptive design that used face-to-face interviews. A total of 342 patients with STEMI were included in this study. To identify symptom clusters, two-step cluster analysis was performed using SPSS software. Multinomial logistic regression to explore factors related to each cluster and multiple logistic regression to determine the effect of symptom clusters on treatment time delay were conducted. Three symptom clusters were identified: cluster 1 (classic MI; characterized by chest pain); cluster 2 (stress symptoms; sweating and chest pain); and cluster 3 (multiple symptoms; dizziness, sweating, chest pain, weakness, and dyspnea). Compared with patients in clusters 2 and 3, those in cluster 1 were more likely to have diabetes or prior MI. Patients in clusters 2 and 3, who predominantly showed other symptoms in addition to chest pain, had a significantly shorter DT and OTB than those in cluster 1. In conclusion, to decrease treatment time delay, it seems important that patients and clinicians recognize symptom clusters, rather than relying on chest pain alone. Further research is necessary to translate our findings into clinical practice and to improve patient education and public education campaigns.

A cluster and inferential analysis of myocardial infarction symptom presentation by age

BACKGROUND

Pre-hospital delay time in myocardial infarction is usually longer in older than in younger patients, with symptom presentation known to be a contributing factor.

AIM

The aim of this article is to examine symptom presentation differences, by age, in patients with myocardial infarction.

METHODS

This is a cross-sectional study using secondary analysis of a multi-site randomised controlled trial on pre-hospital delay time. Data were analysed using logistic regression and factor analysis.

RESULTS

Post-myocardial infarction patients were recruited prior to discharge ( n=1211), 54% were ≥65 years and 80% male. The average number of symptoms was three, with the ≥65 years age group reporting significantly less symptoms. Logistic regression controlling for gender, diabetes and diagnosis with 11 symptoms (χ²=52.09, p<0.001) was significant. Those ≥65 years had less chest symptoms, sweating, stomach upset and left arm pain, in addition to longer pre-hospital delay time. This group also had less symptom clustering and fewer symptoms within atypical clusters. Non-chest clusters occurred in 22% and 18% of the older and younger group respectively. Of note, two clusters 'atypical' (upset stomach/sweating) and 'typical arm' (right and left arm pain symptoms), accounted for 14% and 5% of myocardial infarction presentations in the ≥65 years group, within which 25% and 24% had no chest symptoms.

CONCLUSIONS

The results of this study indicate that myocardial infarction symptom presentation in older patients is likely to be less recognisable and more complex. Increased awareness of the presentation profile of older patients could expedite their triage, diagnosis and, consequently, their prognosis.

Symptoms Suggestive of Acute Coronary Syndrome: When Is Sex Important?

BACKGROUND

Studies have identified sex differences in symptoms of acute coronary syndrome (ACS); however, retrospective designs, abstraction of symptoms from medical records, and variations in assessment forms make it difficult to determine the clinical significance of sex differences.

OBJECTIVE

The aim of this study is to determine the influence of sex on the occurrence and distress of 13 symptoms for patients presenting to the emergency department for symptoms suggestive of ACS.

METHODS

A total of 1064 patients admitted to 5 emergency departments with symptoms triggering a cardiac evaluation were enrolled. Demographic and clinical variables, symptoms, comorbid conditions, and functional status were measured.

RESULTS

The sample was predominantly male (n = 664, 62.4%), white (n = 739, 69.5%), and married (n = 497, 46.9%). Women were significantly older than men (61.3 ± 14.6 vs 59.5 ± 13.6 years). Most patients were discharged with a non-ACS diagnosis (n = 590, 55.5%). Women with ACS were less likely to report chest pain as their chief complaint and to report more nausea (odds ratio [OR], 1.56; confidence interval [CI], 1.00-2.42), shoulder pain (OR, 1.76; CI, 1.13-2.73), and upper back pain (OR, 2.92; CI, 1.81-4.70). Women with ACS experienced more symptoms (6.1 vs 5.5; P = .026) compared with men. Men without ACS had less symptom distress compared with women.

CONCLUSIONS

Women and men evaluated for ACS reported similar rates of chest pain but differed on other classic symptoms. These findings suggest that women and men should be counseled that ACS is not always accompanied by chest pain and multiple symptoms may occur simultaneously.

Fatigue and acute coronary syndrome: a systematic review of contributing factors

Fatigue is a symptom of ACS, but it remains unclear who is at risk and what factors contribute to fatigue. The purpose of the systematic review was to identify factors that influence fatigue in patients with ACS. The review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Literature published from 1981 to 2017 was reviewed, and of 983 articles screened, 36 met inclusion criteria. Variables contributing to fatigue fell into 3 categories: demographic characteristics, clinical characteristics, and other factors. More fatigue was found in women than men, and significant differences in fatigue were identified by race. Additionally, sleep deprivation, depression, and anxiety were associated with higher levels of fatigue. The findings highlight the importance of demographic, clinical, and other factors' impact on fatigue in ACS patients. Fatigue is an important symptom in ACS and healthcare providers must recognize how patient variables affect symptom expression.

Gender Differences in Factors Related to Prehospital Delay in Patients with ST-Segment Elevation Myocardial Infarction

PURPOSE

The aim of our study was to investigate gender differences in factors related to prehospital delay and identify whether the knowledge of acute myocardial infarction symptoms affects this delay in Korean patients with ST-elevation myocardial infarction (STEMI).

MATERIALS AND METHODS

A total of 350 patients (286 men, 64 women) with confirmed STEMI were interviewed to investigate socio-demographics, history of disease, symptom onset time, and factors that contributed to delayed decision time in seeking treatment and hospital arrival time from symptom onset. Factors associated with prehospital delay were examined separately by gender using univariate and multivariate analyses.

RESULTS

Female patients had higher proportions of ≥60-minute decision time and ≥120-minute arrival time compared to male patients (33.9% vs. 23.1%, 60.9% vs. 52.1%, respectively). However, the difference was not statistically significant (p=0.093 and 0.214, respectively). Previous cardiovascular disease (CVD) was associated with increased decision time in men, whereas, in women, lower educational status caused a greater delay in decision time. Factors associated with hospital arrival time excluding delayed decision time were referral from another hospital, previous CVD, and percutaneous coronary intervention in men, and referral from another hospital in women.

CONCLUSION

Gender differences exist in factors related to prehospital delay. Therefore, public education to reduce prehospital delay should be conducted according to gender with a focus on the pertinent factors.

Impact of comorbidities by age on symptom presentation for suspected acute coronary syndromes in the emergency department

BACKGROUND

It is estimated half of acute coronary syndrome (ACS) patients have one or more associated comorbid conditions.

AIMS

Aims were to: 1) examine the prevalence of comorbid conditions in patients presenting to the emergency department with symptoms suggestive of ACS; 2) determine if comorbid conditions influence ACS symptoms; and 3) determine if comorbid conditions predict the likelihood of receiving an ACS diagnosis.

METHODS

A total of 1064 patients admitted to five emergency departments were enrolled in this prospective study. Symptoms were measured on presentation to the emergency department. The Charlson Comorbidity Index (CCI) was used to evaluate group differences in comorbidity burden across demographic traits, risk factors, clinical presentation, and diagnosis.

RESULTS

The most prominent comorbid conditions were prior myocardial infarction, diabetes without target organ damage, and chronic lung disease. In younger ACS patients, higher CCI predicted less chest pain, chest discomfort, unusual fatigue and a lower number of symptoms. In older ACS patients, higher CCI predicted more chest discomfort, upper back pain, abrupt symptom onset, and greater symptom distress. For younger non-ACS patients, higher CCI predicted less chest pain and symptom distress. Higher CCI was associated with a greater likelihood of receiving an ACS diagnosis for younger but not older patients with suspected ACS.

CONCLUSIONS

Younger patients with ACS and higher number of comorbidities report less chest pain, putting them at higher risk for delayed diagnosis and treatment since chest pain is a hallmark symptom for ACS.

Symptom Trajectories After an Emergency Department Visit for Potential Acute Coronary Syndrome

BACKGROUND

Many patients evaluated for acute coronary syndrome (ACS) in emergency departments (EDs) continue to experience troubling symptoms after discharge-regardless of their ultimate medical diagnosis. However, comprehensive understanding of common post-ED symptom trajectories is lacking.

OBJECTIVES

The aim of this study was to identify common trajectories of symptom severity in the 6 months after an ED visit for potential ACS.

METHODS

This was a secondary analysis of data from a larger observational, prospective study conducted in five U.S. EDs. Patients (N = 1005) who had electrocardiogram and biomarker testing ordered, and were identified by the triage nurse as potentially having ACS, were enrolled. Symptom severity was assessed in the hospital after initial stabilization and by telephone at 30 days and 6 months using the validated 13-item ACS Symptom Checklist. Growth mixture modeling was used for the secondary analysis. The eight most commonly reported symptoms (chest discomfort, chest pain, chest pressure, light-headedness, shortness of breath, shoulder pain, unusual fatigue, and upper back pain) were modeled across the three study time points. Models with increasing numbers of classes were compared, and final model selection was based on a combination of interpretability, theoretical justification, and statistical fit indices.

RESULTS

The sample was 62.6% male with a mean age of 60.2 years (SD = 14.17 years), and 57.1% ruled out for ACS. Between two and four distinct trajectory classes were identified for each symptom. The seven different types of trajectories identified across the eight symptoms were labeled "tapering off," "mild/persistent," "moderate/persistent," "moderate/worsening," "moderate/improving," "late onset, "and "severe/improving." Trajectories differed on age, gender, and diagnosis.

DISCUSSION

Research on the individual nature of symptom trajectories can contribute to patient-centered, rather than disease-centered, care. Further research is needed to verify the existence of multiple symptoms trajectories in diverse populations and to assess the antecedents and consequences of individual symptom trajectories.

Symptom clusters in patients presenting to the emergency department with possible acute coronary syndrome differ by sex, age, and discharge diagnosis

OBJECTIVES

To identify classes of individuals presenting to the ED for suspected ACS who shared similar symptoms and clinical characteristics.

BACKGROUND

Describing symptom clusters in undiagnosed patients with suspected ACS is a novel and clinically relevant approach, reflecting real-world emergency department evaluation procedures.

METHODS

Symptoms were measured using a validated 13-item symptom checklist. Latent class analysis was used to describe symptom clusters.

RESULTS

The sample of 874 was 37% female with a mean age of 59.9 years. Four symptom classes were identified: Heavy Symptom Burden (Class 1), Chest Symptoms and Shortness of Breath (Class 2), Chest Symptoms Only (Class 3), and Weary (Class 4). Patients with ACS were more likely to cluster in Classes 2 and 3. Women and younger patients were more likely to group in Class 1.

CONCLUSIONS

Further research is needed to determine the value of symptom clusters in the ED triage and management of suspected ACS.

Disparities in patients presenting to the emergency department with potential acute coronary syndrome: it matters if you are Black or White

OBJECTIVES

To explore disparities between non-Hispanic Blacks and non-Hispanic Whites presenting to the emergency department (ED) with potential acute coronary syndrome (ACS).

BACKGROUND

Individuals with fewer resources have worse health outcomes and these individuals are disproportionately those of color.

METHODS

This prospective study enrolled 663 patients in four EDs. Clinical presentation, treatment, and patient-reported outcome variables were measured at baseline, 1, and 6 months.

RESULTS

Blacks with confirmed ACS were younger; had lower income; less education; more risk factors; more symptoms, and longer prehospital delay at presentation compared to Whites. Blacks experiencing palpitations, unusual fatigue, and chest pain were more than 3 times as likely as Whites to have ACS confirmed. Blacks with ACS had more clinic visits and more symptoms 1 month following discharge.

CONCLUSIONS

Significant racial disparities remain in clinical presentation and outcomes for Blacks compared to Whites presenting to the ED with symptoms suggestive of ACS.

Reliability of the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey among black and white women

BACKGROUND

Coronary heart disease (CHD) mortality rates are higher among women, particularly black, than men. Women's mortality rates may reflect difficulty in recognizing CHD prodromal symptoms (PS) but reliable screening instruments for women are scarce. The McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey (MAPMISS) captures women's PS presentation, but has limited testing among black women.

AIM

To assess the test-retest reliability of the MAPMISS PS section for black and white women.

METHODS

The sample was recruited from women enrolled in a longitudinal study examining the predictive validity of the MAPMISS. The MAPMISS was re-administered to 42 women (22 white, 20 black) 3-5 days after baseline assessment.

RESULTS

Women endorsed an average of 7.5 PS (SD 4.8; range 0-20) initially and 7.6 (SD 4.7; range 0-20) at retest. Over half of the women (54.8%) of both races endorsed the same number of PS at test and retest; for 69%, the number endorsed at both testings differed by no more than one. Percentage agreement and kappa statistics on the number ofPS endorsed were excellent overall and by race. PS test and retest scores, reflecting PS intensity and frequency, were highly correlated overall (r = 0.92, p < 0.001) and separately for white (r = 0.93, p < 0.001) and black women (r = 0.91,p < 0.001). Racial differences were insignificant.

CONCLUSIONS

Findings indicate (i) the MAPMISS PS score has excellent test-retest reliability (r = 0.92) when administered to women without a history of CHD, and (ii) test-retest reliability is as strong for black (r = 0.91) as for white women (r = 0.93).

Latent variable mixture modeling: a flexible statistical approach for identifying and classifying heterogeneity

BACKGROUND

Latent variable mixture modeling is becoming increasingly popular in nursing research, in part due to the sophistication of the method in identifying relationships, patterns, and clusters in the data.

OBJECTIVE

The aim of this study was to provide an overview of mixture modeling techniques, specifically as applied to nursing research, and to present examples from two studies to illustrate how these techniques may be used cross-sectionally and longitudinally.

METHODS

The first data example demonstrates the use of latent profile analysis as applied to the St. George respiratory symptoms questionnaire in 2,232 smokers from the Lovelace Smokers Cohort. The second data example demonstrates growth mixture modeling as applied to condom use trajectories among 728 at-risk adolescents on probation.

RESULTS

Three classes of symptoms emerged among the smokers cohort: those who were high on all symptoms, those who were low on all symptoms, and those who were high on cough and phlegm only. These classes were then distinguishable by participant gender and wood smoke exposure. In the second data example, four classes of condom use emerged. Only 59% of the sample indicated the previously reported decline in condom use over time; condom use remained stable or significantly increased for the remaining 41%.

DISCUSSION

Both sets of results provide additional substantive information about patterns in the data that were not apparent from previously applied traditional methodological techniques. Considerations for the use of latent variable mixture modeling in nursing research are discussed.

Cluster analysis of symptoms in pulmonary arterial hypertension: a pilot study

BACKGROUND

Pulmonary arterial hypertension (PAH) is characterized by elevated pulmonary artery pressures leading to right heart failure and death.

AIMS

The aim of this study was to use cluster analysis to describe the symptom profile in PAH and differences in the health outcomes of health status, health-related quality of life (HRQoL) and psychological states in the cluster groups.

METHODS

A cross-sectional descriptive design was used. A convenience sample completed a socio-demographic and clinical data form, a PAH Symptom Severity and Interference Scale, the Medical Outcomes Study Short Form (SF-36), the United States Cambridge Pulmonary Hypertension Outcome Review (US CAMPHOR) and the Short Form of the Profile of Mood States (POMS).

RESULTS

Of the 151 participants, the mean age was 53.5 ± 15.1 with the majority female (n = 128, 85%). Fifty-eight (41%) were disabled and 67 (44%) were Functional Class IV. The most prevalent symptoms were shortness of breath with exertion (n = 149, 99%) and fatigue (n = 144, 93%). Three clusters emerged: Cluster 1 diffuse symptoms (n = 93), Cluster 2 severe cardiopulmonary symptoms (n = 32) and Cluster 3 moderate cardiopulmonary symptoms (n = 26). Overall, on the SF-36 the participants had poor general health, reduced physical function, role physical, vitality, and a low composite score for physical health. On the POMS the sample had limited vigor and increased fatigue. Cluster 2 Severe Cardiopulmonary Symptoms had worse SF-36, US CAMPHOR and POMS scores than the other cluster groups.

CONCLUSIONS

Patients with PAH are experiencing a constellation of symptoms that are interfering with their life and emerging clusters were present.

Multivariate modeling to identify patterns in clinical data: the example of chest pain

BACKGROUND

In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.

METHODS

We conducted a cross-sectional diagnostic study in 74 primary care practices to establish the validity of symptoms and findings for the diagnosis of coronary heart disease. A total of 1199 patients above age 35 presenting with chest pain were included in the study. General practitioners took a standardized history and performed a physical examination. They also recorded their preliminary diagnoses, investigations and management related to the patient's chest pain. We used multiple correspondence analysis (MCA) to examine associations on variable level, and multidimensional scaling (MDS), k-means and fuzzy cluster analyses to search for subgroups on patient level. We further used heatmaps to graphically illustrate the results.

RESULTS

A multiple correspondence analysis supported our data collection strategy on variable level. Six factors emerged from this analysis: "chest wall syndrome", "vital threat", "stomach and bowel pain", "angina pectoris", "chest infection syndrome", and " self-limiting chest pain". MDS, k-means and fuzzy cluster analysis on patient level were not able to find distinct groups. The resulting cluster solutions were not interpretable and had insufficient statistical quality criteria.

CONCLUSIONS

Chest pain is a heterogeneous clinical category with no coherent associations between signs and symptoms on patient level.